Libraries
library(dplyr)
library(knitr)
library(ggplot2)
library(reshape2)
library(EDAWR)
Data summary
kable(summary(df))
|
Length:3800 |
Min. :1995 |
Length:3800 |
Min. : 0.0 |
Min. : 0 |
Min. : 0.0 |
|
Class :character |
1st Qu.:1999 |
Class :character |
1st Qu.: 25.0 |
1st Qu.: 1128 |
1st Qu.: 84.5 |
|
Mode :character |
Median :2004 |
Mode :character |
Median : 76.0 |
Median : 2589 |
Median : 230.0 |
|
NA |
Mean :2004 |
NA |
Mean : 493.2 |
Mean : 10864 |
Mean : 1253.0 |
|
NA |
3rd Qu.:2009 |
NA |
3rd Qu.: 264.5 |
3rd Qu.: 6706 |
3rd Qu.: 640.0 |
|
NA |
Max. :2013 |
NA |
Max. :25661.0 |
Max. :731540 |
Max. :125991.0 |
|
NA |
NA |
NA |
NA’s :396 |
NA’s :413 |
NA’s :413 |
Tuberculosis cases grouped by gender
group_by_sex_summary <- df %>%
filter(!is.na(child) & !is.na(adult) & !is.na(elderly)) %>%
mutate(all=child + adult + elderly) %>%
group_by(sex) %>%
summarise(cases = sum(all))
kable(group_by_sex_summary)
| female |
15610599 |
| male |
27016456 |
Tuberculosis cases grouped by age
gruped_by_age_and_year <- df %>%
filter(!is.na(child) & !is.na(adult) & !is.na(elderly)) %>%
group_by(year) %>%
summarise(child_cases = sum(child) / 1000, adult_cases = sum(adult) / 1000, elderly_cases = sum(elderly) / 1000)
d <- melt(gruped_by_age_and_year, id.vars="year")
ggplot(d, aes(year,value, col=variable)) +
geom_line() +
labs(x = "Year", y = "Cases [thousands]", color = "Legend") +
theme_linedraw() +
scale_x_continuous(breaks = seq(min(d$year), max(d$year), by = 2))

Tuberculosis cases grouped by age and country
single_graph <- function(df, group) {
melted <- melt(df, id.vars="year")
print(ggplot(melted, aes(year,value, col=variable)) +
geom_line() +
labs(x = "Year", y = "Cases", color = "Legend") +
ggtitle(group$country[1]) +
theme_linedraw() +
scale_x_continuous(breaks = seq(min(df$year), max(df$year), by = 2))
)
}
grouped_by_age_country_year <- df %>%
filter(!is.na(child) & !is.na(adult) & !is.na(elderly)) %>%
group_by(country, year) %>%
summarise(child_cases = sum(child), adult_cases = sum(adult), elderly_cases = sum(elderly)) %>%
group_by(country)
invisible(group_map(grouped_by_age_country_year,single_graph))



































































































